Data Product & Governance Lead

The Gorilla Glue Company; Cincinnati (OH); On-site; USD 150,000 - 190,000; Full time

Job summary

The Gorilla Glue Company seeks a strategic leader for Data Products & Governance. This role drives the enterprise data product portfolio, oversees the master data management program, and leads the data governance program that enables responsible use of data and AI across the organization.

You will steer governance forums, build a federated network of data owners, and partner across IT, Legal, HR, Marketing, Supply Chain, and Finance to translate policy into practical, everyday practice and

Responsibilities

• Lead the enterprise data product portfolio, the master data management program, and the data governance program that supports how data and artificial intelligence are used across the company.
• Lead a team of three data governance and master data specialists, with responsibility for their priorities, development, and day-to-day execution.
• Define the standards those data sets must meet, evaluate fit and quality, and advise on selection; partner with IT, Legal, HR, Marketing, Finance and others to turn written policy into everyday practice.

Job description

What's in it for you?

Thanks to our commitment to creating a supportive and engaged workplace culture, Gorilla Glue has been recognized as a top place to work for 16 years in a row. We offer competitive compensation packages, comprehensive healthcare benefits, and other perks and incentives to ensure our employees feel valued and supported.

What's in it for you?

Thanks to our commitment to creating a supportive and engaged workplace culture, Gorilla Glue has been recognized as a top place to work for 16 years in a row. We offer competitive compensation packages, comprehensive healthcare benefits, and other perks and incentives to ensure our employees feel valued and supported. Joining the Gorilla Glue family means being part of a team passionate about building strong brands and promoting a culture of excellence. As an employee, you'll be able to help create high-quality products and build brands that people love. Join us in an inspiring workplace where you can belong, grow, and make a difference!

Summary Description

The Manager, Data Products & Governance leads the enterprise data product portfolio, the master data management program, and the data governance program that supports how data and artificial intelligence are used across the company. The role has three pillars, described below: data product management, master data management, and data governance and AI enablement. It also leads a team of three data governance and master data specialists, with responsibility for their priorities, development, and day-to-day execution. Together these pillars set the standards, ownership model, and controls that make data trusted, well understood, and appropriately protected; keep master data accurate and consistent across the systems that depend on it; support the practical guardrails that let teams adopt AI capabilities safely and productively rather than informally and invisibly; and prioritize the data product work that turns standards into capabilities people actually use. Beyond the team, the role leads through influence: holding governance forums, developing a federated network of data owners and stewards, and partnering across Information Technology, IT Security, Legal, Human Resources, Marketing, Supply Chain, and Finance to turn written policy into everyday practice. The division of responsibility between this role and the AI Center of Excellence is still being defined; this role brings the data expertise, standards, and controls that AI governance depends on, and partners with the COE on tool, model, and usage policy. Decisions to purchase third-party or syndicated data sets rest with the business functions that hold the business case and the budget. This role defines the standards those data sets must meet, evaluates fit and quality, and advises on selection; it does not decide what is bought.

What will your day look like?

Pillar 1: Data Product Management

• Develop and maintain a prioritized roadmap for enterprise data products, aligned to business strategy and measurable value.
• Partner with business leaders and end users to understand the decisions, workflows, pain points, and opportunities that data products should support.
• Translate business needs into product requirements, user stories, acceptance criteria, and clear outcomes for data engineering, architecture, analytics, and application teams.
• Manage backlog prioritization and make tradeoff recommendations based on business impact, user value, risk, effort, dependencies, and data readiness.
• Guide data products through discovery, design, delivery, launch, adoption, enhancement, and retirement, including user validation and release readiness.
• Drive adoption through stakeholder communication, change planning, documentation, training coordination, and feedback loops.
• Identify opportunities to simplify fragmented reporting and data solutions, increase reuse, and reduce one-off or duplicative work.
• Partner with architecture and engineering to embed governance and product requirements into platform design, integration patterns, and semantic models rather than inspecting for them afterward.

Pillar 2: Master Data Management

• Lead the master data management program across domains such as item, customer, and supplier, setting standards for record creation, enrichment, validation, and change so master data stays accurate and consistent across the ERP and the systems that consume it.
• Govern master data intake and change workflows, including validation, approval routing, and turnaround expectations, so the business gets accurate records at the speed it needs.
• Maintain domain hierarchies, match and merge rules, and record standards so master data supports reporting, planning, and downstream transactions without rework.
• Set data quality standards and measurement for master and transactional data; monitor quality against agreed thresholds and drive root‑cause resolution with the accountable business and technical owners.
• Partner with Supply Chain, Marketing, Finance, and Sales to resolve competing definitions and align master data standards with how each function actually uses the records.

Pillar 3: Data Governance & AI Enablement

• Maintain and evolve the enterprise data governance framework, including the operating model, decision rights, policies, standards, and supporting procedures.
• Partner with Legal & IT Security to apply data classification, access, retention, and data-sharing standards across cloud and on‑premises platforms, including what may be used with AI tools.
• Ensure priority data products carry the ownership, quality, documentation, access, and controls that advanced analytics and AI depend on, so governance and product work reinforce each other rather than compete.
• Partner with the AI COE and cross‑functional teams (Legal, IT Security, HR) to align data and AI policy with regulatory obligations, contractual commitments, and customer and retailer requirements, including input on data and AI terms in vendor and SaaS agreements.
• Apply the data governance exception and escalation process to data and AI requests with documented controls, and monitor industry best practice to advise the AI COE and IT leadership on standards changes as capabilities evolve.

Team Leadership

• Lead a team of data governance and master data specialists: set priorities, balance workload, remove obstacles, and hold a consistent standard for quality and turnaround.
• Coach and develop team members through regular one‑on‑ones, clear expectations, candid feedback, and individual development plans; participate in hiring as the program grows.
• Build the team's capacity to run governance and master data processes independently, so the program does not depend on any single person.
• Define and report program metrics such as master data accuracy and turnaround, data quality performance, issue aging, policy exceptions, data use case throughput, and product adoption, and communicate roadmap, progress, risks, and dependencies to technical and business stakeholders.
• Represent the team's work, capacity, and constraints to IT and business leadership, and lead through influence beyond the team by challenging one‑off requests, surfacing tradeoffs plainly, and building alignment around shared enterprise priorities.
• Serve as a hands‑on player‑coach, directly supporting complex data governance, master data, and data product work while providing practical guidance and quality oversight to team members responsible for day‑to‑day execution.

What you bring to the table: